This paper describes the concept of sensor networks which has been made viable by the convergence of microelectro-mechanical systems technology, wireless communications and digital electronics. First, the sensing tasks and the potential sensor networks applications are explored, and a review of factors influencing the design of sensor networks is provided. Then, the communication architecture for sensor networks is outlined, and the algorithms and protocols developed for each layer in the literature are explored. Open research issues for the realization of sensor networks are

We present a suite of algorithms for self-organization of wireless sensor networks, in which there is a scalably large number of mainly static nodes with highly constrained energy resources. The protocols further support slow mobility by a subset of the nodes, energy-efficient routing, and formation of ad hoc subnetworks for carrying out cooperative signal processing functions among a set of the nodes.

Wireless sensor networks involve very large numbers of small, low-power, wireless devices. Given their unattended nature, and their potential applications in harsh environments, we need a monitoring infrastructure that indicates system failures and resource depletion. In this paper, we briefly describe an architecture for sensor network monitoring, then focus on one aspect of this architecture: continuously computing aggregates (sum, average, count) of network properties (loss rates, energylevels etc., packet counts). Our contributions are two-fold. First, we propose a novel tree construction algorithm that enables energy-efficient computation of some classes of aggregates. Second, we show through actual implementation and experiments that wireless communication artifacts in even relatively benign environments can significantly impact the computation of these aggregate properties. In some cases, without careful attention to detail, the relative error in the computed aggregates can be as much as 50%. However, by carefully discarding links with heavy packet loss and asymmetry, we can improve accuracy by an order of magnitude.

We consider the problem of finding efficient trees to send information from k sources to a single sink in a network where information can be aggregated at intermediate nodes in the tree. Specifically, we assume that if information from j sources is traveling over a link, the total information that needs to be transmitted is f(j). One natural and important (though not necessarily comprehensive) class of functions is those which are concave, non-decreasing, and satisfy f(0) = 0. Our goal is to find a tree which is a good approximation simultaneously to the optimum trees for all such functions. This problem is motivated by aggregation in sensor networks, as well as by buy-at-bulk network design.

We develop distributed algorithms for self-reconfiguring sensor networks that respond to directing a target through a region. The sensor network models the danger levels sensed across its area and has the ability to adapt to changes. It represents the danger areas as obstacles. A protocol that combines the artificial potential field of the sensors with the goal location for the moving object guides the object incrementally across the network to the goal, while maintaining the safest distance to the danger areas. We report on hardware experiments using a physical sensor network consisting of Mote sensors.

It is important to have continuously updated information about network resources and application activities after a wireless sensor network is deployed in unpredictable environment. Such information can help notify users of resource depletion or abnormal activities. However, the low user-to-node ratio and limited energy and bandwidth resources in sensor networks make extracting states from each individual node infeasible. In this paper, we propose an approach to construct abstracted scans of sensor network health by applying in-network aggregation of network states. Specifically, we design a residual energy scan that approximately depicts the remaining energy distribution within a sensor network. Simulations show that our approach has good scalability and energy-efficiency characteristics, compared to continuously extracting the residual energy individually from each node.

Severe energy limitations, and a paucity of computation pose a set of difficult design challenges for sensor networks. Recent progress in two seemingly disparate research areas namely, distributed robotics and low power embedded systems has led to the creation of mobile (or robotic) sensor networks. Autonomous node mobility brings with it its own challenges, but also alleviates some of the traditional problems associated with static sensor networks. We illustrate this by presenting the design of the robomote, a robot platform that functions as a single mobile node in a mobile sensor network. We briefly describe two case studies where the robomote has been used for table top experiments with a mobile sensor network. 1.

Sensor networks not only have the potential to change the way we use, interact with, and view computers, but also the way we use, interact with, and view the world around us. In order to maximize the effectiveness of sensor networks, one has to identify, examine, understand, and provide solutions for the fundamental problems related to wireless embedded sensor networks. We believe that one of such problems is to determine how well the sensor network monitors the instrumented area. These problems are usually classified as coverage problems. There already exist several methods that have been proposed to evaluate a sensor network's coverage. We start from

Wireless ad-hoc sensor networks have the potential to provide the missing interface between the physical world and the Internet, thus impacting a large number of users. This connection will enable computational treatments of the physical world in ways never before possible. In this far reaching scenario, quality of service can be expressed in terms of accuracy and/or latency of observing events and overall state of the physical world. Consequently, one of the fundamental problems in sensor networks is the calculation of coverage, which can be defined as a measure of the ability to detect objects within a sensor filed. Exposure is directly related to coverage in that it is an integral measure of how well the sensor network can observe an object, moving on an arbitrary path, over a period of time.

This paper introduces the application of a sensor network to navigate a flying robot. We have developed distributed algorithms and efficient geographic routing techniques to incrementally guide one or more robots to points of interest based on sensor gradient fields, or along paths defined in terms of Cartesian coordinates. The robot itself is an integral part of the localization process which establishes the positions of sensors which are not known a priori.